Antelop
A user-friendly software package for systems neuroscience data management
Big data in neuroscience
- New technologies such as Neuropixels allow us to record an increasingly high number of units
- Continual shift towards more automated behavioural assays means longer/more recordings
- Scientists have to spend more time on data processing workflows and data management
- Increasingly advanced skills are needed to undertake this (HPC skills, data engineering, etc.)
Data engineering challenges
- Custom file formats/project structures are hard to parse
- Custom preprocessing/analysis scripts are very difficult to reproduce
- Lab data storage not centralised
- High entry barrier to existing tools like DataJoint and NWB which makes their adoption difficult for many labs
Our solution: Antelop
- Software package designed to facilitate the easy adoption of data processing and storage best practices
- Simple pip install and straightforward graphical configuration
- Extensive graphical user interface for all aspects of your data management and processing
- MySQL database backend for centralised storage
- Supports electrophysiology, calcium imaging, and behavioural data processing with HPC integration
Our solution: Antelop
- Integrates with existing tools, such as popular spikesorters, CaImAn, and DeepLabCut
- Leverages the fact that preprocessing requirements for common acquisition methods are fairly uniform
- Implements a range of data visualisation tools and metrics out of the box, including an analysis standard library
- Supports the writing of custom analysis scripts, with direct integration to your lab’s GitHub and data immutability checks for reproducibility
- Has import/export functions for NWB and a range of acquisition systems
- Has an opinionated but accomodating database structure to facilitate large-scale modelling
Infrastructure
Publishing
- Working on a preprint at present
- We aim to publish by May this year
- Python package has been released but is undergoing extensive testing
- Documentation available